Learn how AI is used in energy systems—forecasting demand and generation, predictive maintenance, demand response, and real-time scheduling.
Applied AI for Energy is a live, instructor-led program focused on practical AI use-cases in power and energy systems. You will cover IoT and sensor-driven energy analytics, forecasting and price prediction, optimization and reinforcement learning, and control strategies for demand response and aggregators. The program is case-study driven with a final assessment to validate core concepts and application thinking.
Apply AI techniques to energy data: forecasting, optimization, demand response, pricing mechanisms, and real-time scheduling.
Energy Data Signals
Demand + Generation
Asset Optimization
ANN / DL Models
Control Strategies
Demand Response
Energy Context + AI
Not generic ML—focused on grid, demand, pricing, and control use-cases.
Forecasting Driven
Learn demand, generation, and price prediction foundations used for planning and scheduling.
Decision + Control Thinking
From predictions to actions: incentives, scheduling, and demand response strategies.
How energy systems generate signals and how AI turns them into usable insights.
Design incentives and mechanisms that influence consumption and load shifting.
I had an amazing experience, and thank you, sir, for spending your time with us. I learned so much, especially through the real-time examples provided.
AI methods to detect risk early and optimize asset performance.
Learn the RL mindset for control strategies in energy systems.
Real-time electricity scheduling methods and how forecasts improve DR signals and aggregator services.
Earn a completion certificate and assessment-based evaluation at program end.